Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The purpose of this study is to discuss the harmful use of deepfakes in an organizational context, based on the only two cases the authors found that were addressed by the media from the perspective of corporate fraud. This study offers an overview of deepfake technology, and in particular, examines five W questions to better decipher the impact of these tools on organizations: What is deepfake? Who is the fraudster and who is targeted? Why use them and how? And What after? Based on these five W questions, this study provides an in-depth discussion of the two cases identified. Even though this technology has several advantages, this study examines its dark side. Design/methodology/approach Using comparative analysis, the authors study the only two known and publicized fraud cases by using deepfakes that have targeted chief executive officers to date. Findings The paper provides an extensive picture of the unethical and illicit use of deepfakes in an organizational context and discusses how this technology could affect fraud risk. In addition, the analysis of cases shows that voice-generating software, combined with other fraud schemes such as business email compromise, facilitates the commission of the fraud, as the victims feel confident because they recognize the speaker’s voice and emails. The analysis shows that any organization could be vulnerable to this technology. The median costs of this type of fraud can be high. For the two cases identified, the estimated losses amounted to US$243,000 and US$35,000,000, respectively. Originality/value This paper adds new insights to the scarce research on deepfakes and financial crime by investigating the causes and consequences of the unethical and illicit use of deepfakes. It has several implications for organizations, boards of directors, management and regulatory authorities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it